
"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Union[str, Any]				=      CycleDiffusionPipeline
       UpperCamelCase_		:       int				=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           '''negative_prompt''',
           '''height''',
           '''width''',
           '''negative_prompt_embeds''',
       }
       UpperCamelCase_		:       Union[str, Any]				=      PipelineTesterMixin.required_optional_params - {'''latents'''}
       UpperCamelCase_		:       Optional[int]				=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}						)
       UpperCamelCase_		:       Dict				=      IMAGE_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase_		:       str				=      IMAGE_TO_IMAGE_IMAGE_PARAMS






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	int:







              """simple docstring"""
              torch.manual_seed(0      )
              UpperCamelCase       			=		UNetaDConditionModel(
                  block_out_channels=(32, 64)							,   layers_per_block=2							,   sample_size=32							,   in_channels=4							,   out_channels=4							,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,   cross_attention_dim=32							,   )
              UpperCamelCase       			=		DDIMScheduler(
                  beta_start=0.00085							,   beta_end=0.012							,   beta_schedule="scaled_linear"							,   num_train_timesteps=1_000							,   clip_sample=UpperCAmelCase_							,   set_alpha_to_one=UpperCAmelCase_							,   )
              torch.manual_seed(0      )
              UpperCamelCase       			=		AutoencoderKL(
                  block_out_channels=[32, 64]							,   in_channels=3							,   out_channels=3							,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,   latent_channels=4							,   )
              torch.manual_seed(0      )
              UpperCamelCase       			=		CLIPTextConfig(
                  bos_token_id=0							,   eos_token_id=2							,   hidden_size=32							,   intermediate_size=37							,   layer_norm_eps=1e-05							,   num_attention_heads=4							,   num_hidden_layers=5							,   pad_token_id=1							,   vocab_size=1_000							,   )
              UpperCamelCase       			=		CLIPTextModel(UpperCAmelCase_      )
              UpperCamelCase       			=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

              UpperCamelCase       			=		{
                  "unet": unet,
                  "scheduler": scheduler,
                  "vae": vae,
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "safety_checker": None,
                  "feature_extractor": None,
              }
              return components






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				Union[str, Any]=0      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		floats_tensor((1, 3, 32, 32)							,   rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )
              UpperCamelCase       			=		image / 2 + 0.5
              if str(UpperCAmelCase_      ).startswith("mps"      ):
                     UpperCamelCase       			=		torch.manual_seed(UpperCAmelCase_      )
              else:
                     UpperCamelCase       			=		torch.Generator(device=UpperCAmelCase_      ).manual_seed(UpperCAmelCase_      )
              UpperCamelCase       			=		{
                  "prompt": "An astronaut riding an elephant",
                  "source_prompt": "An astronaut riding a horse",
                  "image": image,
                  "generator": generator,
                  "num_inference_steps": 2,
                  "eta": 0.1,
                  "strength": 0.8,
                  "guidance_scale": 3,
                  "source_guidance_scale": 1,
                  "output_type": "numpy",
              }
              return inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		"cpu"  # ensure determinism for the device-dependent torch.Generator

              UpperCamelCase       			=		self.get_dummy_components()
              UpperCamelCase       			=		CycleDiffusionPipeline(**UpperCAmelCase_      )
              UpperCamelCase       			=		pipe.to(UpperCAmelCase_      )
              pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

              UpperCamelCase       			=		self.get_dummy_inputs(UpperCAmelCase_      )
              UpperCamelCase       			=		pipe(**UpperCAmelCase_      )
              UpperCamelCase       			=		output.images

              UpperCamelCase       			=		images[0, -3:, -3:, -1]

              assert images.shape == (1, 32, 32, 3)
              UpperCamelCase       			=		np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]      )

              assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






       @unittest.skipIf(torch_device != "cuda"							,   "This test requires a GPU"      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.get_dummy_components()
              for name, module in components.items():
                     if hasattr(UpperCAmelCase_							,   "half"      ):
                            UpperCamelCase       			=		module.half()
              UpperCamelCase       			=		CycleDiffusionPipeline(**UpperCAmelCase_      )
              UpperCamelCase       			=		pipe.to(UpperCAmelCase_      )
              pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

              UpperCamelCase       			=		self.get_dummy_inputs(UpperCAmelCase_      )
              UpperCamelCase       			=		pipe(**UpperCAmelCase_      )
              UpperCamelCase       			=		output.images

              UpperCamelCase       			=		images[0, -3:, -3:, -1]

              assert images.shape == (1, 32, 32, 3)
              UpperCamelCase       			=		np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]      )

              assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






       @skip_mps
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Any:







              """simple docstring"""
              return super().test_save_load_local()






       @unittest.skip("non-deterministic pipeline"      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	str:







              """simple docstring"""
              return super().test_inference_batch_single_identical()






       @skip_mps
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              return super().test_dict_tuple_outputs_equivalent()






       @skip_mps
       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	List[str]:







              """simple docstring"""
              return super().test_save_load_optional_components()






       @skip_mps
       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Union[str, Any]:







              """simple docstring"""
              return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class 	__a	(   unittest.TestCase						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	List[str]:







              """simple docstring"""
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/cycle-diffusion/black_colored_car.png"      )
              UpperCamelCase       			=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"      )
              UpperCamelCase       			=		init_image.resize((512, 512)      )

              UpperCamelCase       			=		"CompVis/stable-diffusion-v1-4"
              UpperCamelCase       			=		DDIMScheduler.from_pretrained(UpperCAmelCase_							,   subfolder="scheduler"      )
              UpperCamelCase       			=		CycleDiffusionPipeline.from_pretrained(
                  UpperCAmelCase_							,   scheduler=UpperCAmelCase_							,   safety_checker=UpperCAmelCase_							,   torch_dtype=torch.floataa							,   revision="fp16"      )

              pipe.to(UpperCAmelCase_      )
              pipe.set_progress_bar_config(disable=UpperCAmelCase_      )
              pipe.enable_attention_slicing()

              UpperCamelCase       			=		"A black colored car"
              UpperCamelCase       			=		"A blue colored car"

              UpperCamelCase       			=		torch.manual_seed(0      )
              UpperCamelCase       			=		pipe(
                  prompt=UpperCAmelCase_							,   source_prompt=UpperCAmelCase_							,   image=UpperCAmelCase_							,   num_inference_steps=100							,   eta=0.1							,   strength=0.85							,   guidance_scale=3							,   source_guidance_scale=1							,   generator=UpperCAmelCase_							,   output_type="np"							,   )
              UpperCamelCase       			=		output.images

              # the values aren't exactly equal, but the images look the same visually
              assert np.abs(image - expected_image      ).max() < 5e-1






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/cycle-diffusion/black_colored_car.png"      )
              UpperCamelCase       			=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"      )
              UpperCamelCase       			=		init_image.resize((512, 512)      )

              UpperCamelCase       			=		"CompVis/stable-diffusion-v1-4"
              UpperCamelCase       			=		DDIMScheduler.from_pretrained(UpperCAmelCase_							,   subfolder="scheduler"      )
              UpperCamelCase       			=		CycleDiffusionPipeline.from_pretrained(UpperCAmelCase_							,   scheduler=UpperCAmelCase_							,   safety_checker=UpperCAmelCase_      )

              pipe.to(UpperCAmelCase_      )
              pipe.set_progress_bar_config(disable=UpperCAmelCase_      )
              pipe.enable_attention_slicing()

              UpperCamelCase       			=		"A black colored car"
              UpperCamelCase       			=		"A blue colored car"

              UpperCamelCase       			=		torch.manual_seed(0      )
              UpperCamelCase       			=		pipe(
                  prompt=UpperCAmelCase_							,   source_prompt=UpperCAmelCase_							,   image=UpperCAmelCase_							,   num_inference_steps=100							,   eta=0.1							,   strength=0.85							,   guidance_scale=3							,   source_guidance_scale=1							,   generator=UpperCAmelCase_							,   output_type="np"							,   )
              UpperCamelCase       			=		output.images

              assert np.abs(image - expected_image      ).max() < 2e-2







"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=7					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		None
       if token is not None:
              UpperCamelCase       			=		{"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

       # The id of a workflow (not of a workflow run)
       UpperCamelCase       			=		"636036"

       UpperCamelCase       			=		F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
       # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
       url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

       UpperCamelCase       			=		requests.get(UpperCAmelCase_		,   headers=UpperCAmelCase_					).json()

       return result["workflow_runs"]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		get_daily_ci_runs(UpperCAmelCase_					)
       UpperCamelCase       			=		None
       for workflow_run in workflow_runs:
              if workflow_run["status"] == "completed":
                     UpperCamelCase       			=		workflow_run["id"]
                     break

       return workflow_run_id



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		get_last_daily_ci_runs(UpperCAmelCase_					)
       if workflow_run_id is not None:
              UpperCamelCase       			=		get_artifacts_links(worflow_run_id=UpperCAmelCase_		,   token=UpperCAmelCase_					)
              for artifact_name in artifact_names:
                     if artifact_name in artifacts_links:
                            UpperCamelCase       			=		artifacts_links[artifact_name]
                            download_artifact(
                                artifact_name=UpperCAmelCase_		,   artifact_url=UpperCAmelCase_		,   output_dir=UpperCAmelCase_		,   token=UpperCAmelCase_					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       get_last_daily_ci_artifacts(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       UpperCamelCase       			=		{}
       for artifact_name in artifact_names:
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_		,   F"{artifact_name}.zip"					)
              if os.path.isfile(UpperCAmelCase_					):
                     UpperCamelCase       			=		{}
                     with zipfile.ZipFile(UpperCAmelCase_					) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(UpperCAmelCase_					):
                                          # read the file
                                          with z.open(UpperCAmelCase_					) as f:
                                                 UpperCamelCase       			=		f.read().decode("UTF-8"					)

       return results





